Zhenshuai Wan^1,2,*, Longwang Yue², Yanfeng Wang², Pu Zhao²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1047-1065, 2024, DOI:10.32604/cmes.2024.053773

Abstract Hydraulic actuators are highly nonlinear when they are subjected to different types of model uncertainties and dynamic disturbances. These unfavorable factors adversely affect the control performance of the hydraulic actuator. Although various control methods have been employed to improve the tracking precision of the dynamic system, optimizing and adjusting control gain to mitigate the hydraulic actuator model uncertainties remains elusive. This study presents an adaptive back-stepping sliding mode controller (ABSMC) to enhance the trajectory tracking precision, where the virtual control law is constructed to replace the position error. The adaptive control theory is introduced in More >

Vin Cent Tai^1,*, Yong Chai Tan¹, Nor Faiza Abd Rahman¹, Hui Xin Che², Chee Ming Chia², Lip Huat Saw³, Mohd Fozi Ali⁴

Energy Engineering, Vol.118, No.3, pp. 715-725, 2021, DOI:10.32604/EE.2021.014865

Abstract Electricity demand is also known as load in electric power system. This article presents a Long-Term Load Forecasting (LTLF) approach for Malaysia. An Artificial Neural Network (ANN) of 5-layer Multi-Layered Perceptron (MLP) structure has been designed and tested for this purpose. Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030. Pearson correlation was used to examine the input variables for model construction. The analysis indicates that Primary Energy Supply (PES), population, Gross Domestic Product (GDP) and temperature are strongly correlated. The forecast results by the proposed… More >